"negimage_landgrabbing",
"negimage_productquality",
"negimage_resourceextraction",
"negimage_takingjobsbusiness")
# Load/Prep Coefficient Results ------------------------------------------------
df_results <- readRDS(file.path(data_results_file_path,
paste0("models_type_", 1),
"coefficients",
"coefficients.Rds"))
df_results <- df_results[is.na(df_results$plcompltd) | df_results$plcompltd %in% F,]
df_results <- df_results %>%
filter(buffer == 30) %>%
filter(is.na(planned_year) | planned_year %in% 2010) %>%
filter(is.na(infrastructure))
df_results <- df_results %>%
mutate(completed_planned =
case_when(grepl("Completed",var) ~ "completed",
grepl("Planned",var) ~ "planned"),
country = var %>%
str_replace_all("Completed|Planned", "") %>%
str_replace_all(" Aid", "") %>%
str_squish() %>%
tolower()) %>%
dplyr::select(-var) %>%
pivot_wider(names_from = completed_planned,
values_from = c(coef, ci2_5, ci97_5))
df_results <- df_results %>%
filter(subset %in% c("full", "restricted"),
dv %in% c(fig1_vars,
fig2_vars,
fig3_vars,
fig4_vars,
fig5_vars))
# Average DVs ==================================================================
# 1. Load data and subset to complete observations (with covariates)
# 2. Take average across different aid groups
# -- 2.1 All data
# -- 2.2 Near Planned: usaid
# -- 2.3 Near Planned: china.pl10
# -- 2.4 Near Planned: china.plNA
# 3. Merge and append
# 1. Load ----------------------------------------------------------------------
df <- readRDS(file.path(data_file_path, "afro_china_data.Rds"))
df$lib_dem_val_index[df$afro.round %in% 6] <- NA # Only use rounds 2-5
df <- df %>%
filter(afro.round != 1) %>%
filter(!is.na(age),
!is.na(muslim),
!is.na(urban),
!is.na(male),
!is.na(distance_capital),
!is.na(in_leader_adm1))
# 2 Take average across different aid groups -----------------------------------
# 2.1 All data -----------------------------------------------------------------
df_full <- df %>%
filter(sample_full %in% T) %>%
dplyr::summarise_all(mean, na.rm = T) %>%
mutate(subset = "full")
df_restricted <- df %>%
filter(sample_restricted %in% T) %>%
dplyr::summarise_all(mean, na.rm = T) %>%
mutate(subset = "restricted")
dvs_means_all_df <- bind_rows(df_full,
df_restricted) %>%
pivot_longer(cols = -subset) %>%
#mutate(aid_subset = "all") %>%
filter(name %in% c(fig1_vars,
fig2_vars,
fig3_vars,
fig4_vars,
fig5_vars))
# 2.2 Near Planned: usaid ------------------------------------------------------
df_full <- df %>%
filter(sample_full %in% T,
planned_near_usaid.30km.bin %in% T) %>%
dplyr::summarise_all(mean, na.rm = T) %>%
mutate(subset = "full")
df_restricted <- df %>%
filter(sample_restricted %in% T,
planned_near_usaid.30km.bin %in% T) %>%
dplyr::summarise_all(mean, na.rm = T) %>%
mutate(subset = "restricted")
dvs_means_usaid_df <- bind_rows(df_full,
df_restricted) %>%
pivot_longer(cols = -subset) %>%
#mutate(aid_subset = "usaid") %>%
filter(name %in% c(fig1_vars,
fig2_vars,
fig3_vars,
fig4_vars,
fig5_vars))
# 2.3 Near Planned: china.pl10 -------------------------------------------------
df_full <- df %>%
filter(sample_full %in% T,
planned_near_china.pl10.30km.bin %in% T) %>%
dplyr::summarise_all(mean, na.rm = T) %>%
mutate(subset = "full")
df_restricted <- df %>%
filter(sample_restricted %in% T,
planned_near_china.pl10.30km.bin %in% T) %>%
dplyr::summarise_all(mean, na.rm = T) %>%
mutate(subset = "restricted")
dvs_means_china.pl10_df <- bind_rows(df_full,
df_restricted) %>%
pivot_longer(cols = -subset) %>%
#mutate(aid_subset = "china.pl10") %>%
filter(name %in% c(fig1_vars,
fig3_vars,
fig4_vars,
fig5_vars))
# 2.4 Near Planned: china.plNA -------------------------------------------------
df_full <- df %>%
filter(sample_full %in% T,
planned_near_china.plNA.30km.bin %in% T) %>%
dplyr::summarise_all(mean, na.rm = T) %>%
mutate(subset = "full")
df_restricted <- df %>%
filter(sample_restricted %in% T,
planned_near_china.plNA.30km.bin %in% T) %>%
dplyr::summarise_all(mean, na.rm = T) %>%
mutate(subset = "restricted")
dvs_means_china.plNA_df <- bind_rows(df_full,
df_restricted) %>%
pivot_longer(cols = -subset) %>%
filter(name %in% fig2_vars)
# 3. Merge and append ----------------------------------------------------------
dvs_means_usaid_df <- dvs_means_usaid_df %>%
mutate(country = "usa")
dvs_means_china_df <- bind_rows(dvs_means_china.pl10_df,
dvs_means_china.plNA_df) %>%
mutate(country = "chinese")
dvs_means_plannedcountry_df <- bind_rows(dvs_means_usaid_df,
dvs_means_china_df) %>%
dplyr::rename(dv_value_planned = value,
dv = name)
dvs_means_all_df <- dvs_means_all_df %>%
dplyr::rename(dv_value_all = value,
dv = name)
df_results <- merge(df_results, dvs_means_all_df, by = c("dv", "subset"), all = T)
df_results <- merge(df_results, dvs_means_plannedcountry_df, by = c("dv", "subset", "country"), all = T)
df_results <- df_results %>%
filter(!is.na(dv_clean)) # posistive/negative vars -- no usa, so NA here
# TABLE ========================================================================
df_results <- df_results %>%
mutate(coef_diff       = coef_completed - coef_planned,
pchange_all     = (coef_diff)/dv_value_all * 100,
pchange_planned = (coef_diff)/dv_value_planned * 100)
df_results <- df_results %>%
dplyr::select(subset,
dv,
dv_clean,
country,
coef_diff,
dv_value_all,
dv_value_planned,
pchange_all,
pchange_planned) %>%
mutate(country = case_when(country %in% "chinese" ~ "Chinese",
country %in% "usa" ~ "US"),
dv_clean = dv_clean %>% str_replace_all("\\n", "  ") %>%
str_replace_all("Positive Image: ", "") %>%
str_replace_all("Negative Image: ", "") %>%
str_squish() %>%
str_squish())
pull_tex <- function(dv,
subset,
country,
coef_round = 2){
df_results_i <- df_results[((df_results$dv %in% dv) &
(df_results$subset %in% subset) &
(df_results$country %in% country)),]
df_results_i <- df_results_i %>%
mutate(tex = paste(dv_clean,
country,
coef_diff %>% round(coef_round) %>% format(scientific=F),
dv_value_all %>% round(2),
pchange_all %>% round(1) %>% paste0("\\%"),
dv_value_planned %>% round(2),
pchange_planned %>% round(1) %>% paste0("\\%"),
sep = " & ") %>% paste("\\\\ \n "))
df_results_i %>%
pull(tex) %>%
cat()
}
sink("~/Desktop/test/tables/table.tex")
cat("\\begin{tabular}{lcc | cc  | cc} \n ")
cat("\\hline \n ")
cat("Dependent Variable & Aid & Coef $\\Delta$  & \\multicolumn{2}{c|}{All} & \\multicolumn{2}{c}{Respondents Near} \\\\ \n ")
cat("                   &     & [Compl. - Pln.] & \\multicolumn{2}{c|}{Respondents} & \\multicolumn{2}{c}{Planned Project} \\\\ \n ")
cat("\\hline \n ")
cat("                   &     &                & DV   & \\% $\\Delta$ & DV   & \\% $\\Delta$ \\\\ \n ")
cat("                   &     &                & Mean & from DV       & Mean & from DV \\\\ \n ")
cat("\\hline \n ")
# FIGURE 1 - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
cat("\\multicolumn{5}{l|}{\\bf Effects of Chinese and US aid on perceptions of China and the US} & \\multicolumn{2}{l}{} \\\\ \n ")
cat("\\multicolumn{3}{l|}{\\emph{Full Sample}} & \\multicolumn{2}{c|}{} & \\multicolumn{2}{c}{} \\\\ \n")
pull_tex("china_positive_influence_index", "full", "Chinese")
pull_tex("china.best.dev.model",           "full", "Chinese")
pull_tex("usa.best.dev.model",             "full", "Chinese")
cat("\\multicolumn{3}{l|}{} & \\multicolumn{2}{c|}{} & \\multicolumn{2}{c}{} \\\\ \n") # Blank
cat("\\multicolumn{3}{l|}{\\emph{Restricted Sample}} & \\multicolumn{2}{c|}{} & \\multicolumn{2}{c}{} \\\\ \n")
pull_tex("china_positive_influence_index", "restricted", "Chinese")
pull_tex("china.best.dev.model",           "restricted", "Chinese")
pull_tex("usa.best.dev.model",             "restricted", "Chinese")
pull_tex("china_positive_influence_index", "restricted", "US")
pull_tex("china.best.dev.model",           "restricted", "US")
pull_tex("usa.best.dev.model",             "restricted", "US")
# FIGURE 2 - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
cat("\\multicolumn{3}{l|}{} & \\multicolumn{2}{c|}{} & \\multicolumn{2}{c}{} \\\\ \n") # Blank
cat("\\multicolumn{3}{l|}{\\bf Effects of Chinese and US aid on liberal democratic values} & \\multicolumn{2}{l|}{} & \\multicolumn{2}{l}{} \\\\ \n ")
cat("\\multicolumn{3}{l|}{\\emph{Full Sample}} & \\multicolumn{2}{c|}{} & \\multicolumn{2}{c}{} \\\\ \n")
pull_tex("lib_dem_val_index", "full", "Chinese")
cat("\\multicolumn{3}{l|}{} & \\multicolumn{2}{c|}{} & \\multicolumn{2}{c}{} \\\\ \n") # Blank
cat("\\multicolumn{3}{l|}{\\emph{Restricted Sample}} & \\multicolumn{2}{c|}{} & \\multicolumn{2}{c}{} \\\\ \n")
pull_tex("lib_dem_val_index", "restricted", "Chinese")
pull_tex("lib_dem_val_index", "restricted", "US")
# FIGURE 3 - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
cat("\\multicolumn{3}{l|}{} & \\multicolumn{2}{c|}{} & \\multicolumn{2}{c}{} \\\\ \n") # Blank
cat("\\multicolumn{5}{l|}{\\bf Effects of Chinese and US aid on perceptions of former colonial powers} & \\multicolumn{2}{l}{} \\\\ \n ")
cat("\\multicolumn{3}{l|}{\\emph{Full Sample}} & \\multicolumn{2}{c|}{} & \\multicolumn{2}{c}{} \\\\ \n")
pull_tex("formcolnpower.best.dev.model", "full", "Chinese")
cat("\\multicolumn{3}{l|}{} & \\multicolumn{2}{c|}{} & \\multicolumn{2}{c}{} \\\\ \n") # Blank
cat("\\multicolumn{3}{l|}{\\emph{Restricted Sample}} & \\multicolumn{2}{c|}{} & \\multicolumn{2}{c}{} \\\\ \n")
pull_tex("formcolnpower.best.dev.model", "restricted", "Chinese")
pull_tex("formcolnpower.best.dev.model", "restricted", "US")
# FIGURE 4 - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
cat("\\multicolumn{3}{l|}{} & \\multicolumn{2}{c|}{} & \\multicolumn{2}{c}{} \\\\ \n") # Blank
cat("\\multicolumn{5}{l|}{\\bf Effects of Chinese aid on factors contributing to positive image of China} & \\multicolumn{2}{l}{} \\\\ \n ")
cat("\\multicolumn{3}{l|}{\\emph{Full Sample}} & \\multicolumn{2}{c|}{} & \\multicolumn{2}{c}{} \\\\ \n")
pull_tex("posimage_productcost", "full", "Chinese")
pull_tex("posimage_supportinintlaffiars", "full", "Chinese")
pull_tex("posimage_noninterference", "full", "Chinese")
pull_tex("posimage_chinesepeople", "full", "Chinese")
pull_tex("posimage_infordevinvetment", "full", "Chinese")
pull_tex("posimage_businessinvetment", "full", "Chinese")
# FIGURE 5 - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
cat("\\multicolumn{3}{l|}{} & \\multicolumn{2}{c|}{} & \\multicolumn{2}{c}{} \\\\ \n") # Blank
cat("\\multicolumn{5}{l|}{\\bf Effects of Chinese aid on factors contributing to negative image of China} & \\multicolumn{2}{l}{} \\\\ \n ")
cat("\\multicolumn{3}{l|}{\\emph{Full Sample}} & \\multicolumn{2}{c|}{} & \\multicolumn{2}{c}{} \\\\ \n")
pull_tex("negimage_productquality", "full", "Chinese")
pull_tex("negimage_landgrabbing", "full", "Chinese")
pull_tex("negimage_takingjobsbusiness", "full", "Chinese")
pull_tex("negimage_resourceextraction", "full", "Chinese")
pull_tex("negimage_cooperateundemocratic", "full", "Chinese")
pull_tex("negimage_chinesecitizenbehavior", "full", "Chinese", 4)
cat("\\hline \n ")
cat("\\end{tabular} ")
sink()
# Foreign Aid and State Legitimacy in Africa: Cross-National and Sub-National
# Evidence from Surveys, Survey Experiments, and Behavioral Games
# Table A.14: Magnitude of Impacts Table
fig1_vars <- c("china_positive_influence_index",
"china.best.dev.model",
"usa.best.dev.model")
fig2_vars <- c("lib_dem_val_index")
fig3_vars <- c("formcolnpower.best.dev.model")
fig4_vars <- c("posimage_businessinvetment",
"posimage_chinesepeople",
"posimage_infordevinvetment",
"posimage_noninterference",
"posimage_productcost",
"posimage_supportinintlaffiars")
fig5_vars <- c("negimage_chinesecitizenbehavior",
"negimage_cooperateundemocratic",
"negimage_landgrabbing",
"negimage_productquality",
"negimage_resourceextraction",
"negimage_takingjobsbusiness")
# Load/Prep Coefficient Results ------------------------------------------------
df_results <- readRDS(file.path(data_results_file_path,
paste0("models_type_", 1),
"coefficients",
"coefficients.Rds"))
df_results <- df_results[is.na(df_results$plcompltd) | df_results$plcompltd %in% F,]
df_results <- df_results %>%
filter(buffer == 30) %>%
filter(is.na(planned_year) | planned_year %in% 2010) %>%
filter(is.na(infrastructure))
df_results <- df_results %>%
mutate(completed_planned =
case_when(grepl("Completed",var) ~ "completed",
grepl("Planned",var) ~ "planned"),
country = var %>%
str_replace_all("Completed|Planned", "") %>%
str_replace_all(" Aid", "") %>%
str_squish() %>%
tolower()) %>%
dplyr::select(-var) %>%
pivot_wider(names_from = completed_planned,
values_from = c(coef, ci2_5, ci97_5))
df_results <- df_results %>%
filter(subset %in% c("full", "restricted"),
dv %in% c(fig1_vars,
fig2_vars,
fig3_vars,
fig4_vars,
fig5_vars))
# Average DVs ==================================================================
# 1. Load data and subset to complete observations (with covariates)
# 2. Take average across different aid groups
# -- 2.1 All data
# -- 2.2 Near Planned: usaid
# -- 2.3 Near Planned: china.pl10
# -- 2.4 Near Planned: china.plNA
# 3. Merge and append
# 1. Load ----------------------------------------------------------------------
df <- readRDS(file.path(data_file_path, "afro_china_data.Rds"))
df$lib_dem_val_index[df$afro.round %in% 6] <- NA # Only use rounds 2-5
df <- df %>%
filter(afro.round != 1) %>%
filter(!is.na(age),
!is.na(muslim),
!is.na(urban),
!is.na(male),
!is.na(distance_capital),
!is.na(in_leader_adm1))
# 2 Take average across different aid groups -----------------------------------
# 2.1 All data -----------------------------------------------------------------
df_full <- df %>%
filter(sample_full %in% T) %>%
dplyr::summarise_all(mean, na.rm = T) %>%
mutate(subset = "full")
df_restricted <- df %>%
filter(sample_restricted %in% T) %>%
dplyr::summarise_all(mean, na.rm = T) %>%
mutate(subset = "restricted")
dvs_means_all_df <- bind_rows(df_full,
df_restricted) %>%
pivot_longer(cols = -subset) %>%
#mutate(aid_subset = "all") %>%
filter(name %in% c(fig1_vars,
fig2_vars,
fig3_vars,
fig4_vars,
fig5_vars))
# 2.2 Near Planned: usaid ------------------------------------------------------
df_full <- df %>%
filter(sample_full %in% T,
planned_near_usaid.30km.bin %in% T) %>%
dplyr::summarise_all(mean, na.rm = T) %>%
mutate(subset = "full")
df_restricted <- df %>%
filter(sample_restricted %in% T,
planned_near_usaid.30km.bin %in% T) %>%
dplyr::summarise_all(mean, na.rm = T) %>%
mutate(subset = "restricted")
dvs_means_usaid_df <- bind_rows(df_full,
df_restricted) %>%
pivot_longer(cols = -subset) %>%
#mutate(aid_subset = "usaid") %>%
filter(name %in% c(fig1_vars,
fig2_vars,
fig3_vars,
fig4_vars,
fig5_vars))
# 2.3 Near Planned: china.pl10 -------------------------------------------------
df_full <- df %>%
filter(sample_full %in% T,
planned_near_china.pl10.30km.bin %in% T) %>%
dplyr::summarise_all(mean, na.rm = T) %>%
mutate(subset = "full")
df_restricted <- df %>%
filter(sample_restricted %in% T,
planned_near_china.pl10.30km.bin %in% T) %>%
dplyr::summarise_all(mean, na.rm = T) %>%
mutate(subset = "restricted")
dvs_means_china.pl10_df <- bind_rows(df_full,
df_restricted) %>%
pivot_longer(cols = -subset) %>%
#mutate(aid_subset = "china.pl10") %>%
filter(name %in% c(fig1_vars,
fig3_vars,
fig4_vars,
fig5_vars))
# 2.4 Near Planned: china.plNA -------------------------------------------------
df_full <- df %>%
filter(sample_full %in% T,
planned_near_china.plNA.30km.bin %in% T) %>%
dplyr::summarise_all(mean, na.rm = T) %>%
mutate(subset = "full")
df_restricted <- df %>%
filter(sample_restricted %in% T,
planned_near_china.plNA.30km.bin %in% T) %>%
dplyr::summarise_all(mean, na.rm = T) %>%
mutate(subset = "restricted")
dvs_means_china.plNA_df <- bind_rows(df_full,
df_restricted) %>%
pivot_longer(cols = -subset) %>%
filter(name %in% fig2_vars)
# 3. Merge and append ----------------------------------------------------------
dvs_means_usaid_df <- dvs_means_usaid_df %>%
mutate(country = "usa")
dvs_means_china_df <- bind_rows(dvs_means_china.pl10_df,
dvs_means_china.plNA_df) %>%
mutate(country = "chinese")
dvs_means_plannedcountry_df <- bind_rows(dvs_means_usaid_df,
dvs_means_china_df) %>%
dplyr::rename(dv_value_planned = value,
dv = name)
dvs_means_all_df <- dvs_means_all_df %>%
dplyr::rename(dv_value_all = value,
dv = name)
df_results <- merge(df_results, dvs_means_all_df, by = c("dv", "subset"), all = T)
df_results <- merge(df_results, dvs_means_plannedcountry_df, by = c("dv", "subset", "country"), all = T)
df_results <- df_results %>%
filter(!is.na(dv_clean)) # posistive/negative vars -- no usa, so NA here
# TABLE ========================================================================
df_results <- df_results %>%
mutate(coef_diff       = coef_completed - coef_planned,
pchange_all     = (coef_diff)/dv_value_all * 100,
pchange_planned = (coef_diff)/dv_value_planned * 100)
df_results <- df_results %>%
dplyr::select(subset,
dv,
dv_clean,
country,
coef_diff,
dv_value_all,
dv_value_planned,
pchange_all,
pchange_planned) %>%
mutate(country = case_when(country %in% "chinese" ~ "Chinese",
country %in% "usa" ~ "US"),
dv_clean = dv_clean %>% str_replace_all("\\n", "  ") %>%
str_replace_all("Positive Image: ", "") %>%
str_replace_all("Negative Image: ", "") %>%
str_squish() %>%
str_squish())
pull_tex <- function(dv,
subset,
country,
coef_round = 2){
df_results_i <- df_results[((df_results$dv %in% dv) &
(df_results$subset %in% subset) &
(df_results$country %in% country)),]
df_results_i <- df_results_i %>%
mutate(tex = paste(dv_clean,
country,
coef_diff %>% round(coef_round) %>% format(scientific=F),
dv_value_all %>% round(2),
pchange_all %>% round(1) %>% paste0("\\%"),
dv_value_planned %>% round(2),
pchange_planned %>% round(1) %>% paste0("\\%"),
sep = " & ") %>% paste("\\\\ \n "))
df_results_i %>%
pull(tex) %>%
cat()
}
sink("~/Desktop/test/tables/table.tex")
cat("\\begin{tabular}{lcc | cc  | cc} \n ")
cat("\\hline \n ")
cat("Dependent Variable & Aid & Coef $\\Delta$  & \\multicolumn{2}{c|}{All} & \\multicolumn{2}{c}{Respondents Near} \\\\ \n ")
cat("                   &     & [Compl. - Pln.] & \\multicolumn{2}{c|}{Respondents} & \\multicolumn{2}{c}{Planned Project} \\\\ \n ")
cat("\\hline \n ")
cat("                   &     &                & DV   & \\% $\\Delta$ & DV   & \\% $\\Delta$ \\\\ \n ")
cat("                   &     &                & Mean & from DV       & Mean & from DV \\\\ \n ")
cat("\\hline \n ")
# FIGURE 1 - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
cat("\\multicolumn{5}{l|}{\\bf Effects of Chinese and US aid on perceptions of China and the US} & \\multicolumn{2}{l}{} \\\\ \n ")
cat("\\multicolumn{3}{l|}{\\emph{Full Sample}} & \\multicolumn{2}{c|}{} & \\multicolumn{2}{c}{} \\\\ \n")
pull_tex("china_positive_influence_index", "full", "Chinese")
pull_tex("china.best.dev.model",           "full", "Chinese")
pull_tex("usa.best.dev.model",             "full", "Chinese")
cat("\\multicolumn{3}{l|}{} & \\multicolumn{2}{c|}{} & \\multicolumn{2}{c}{} \\\\ \n") # Blank
cat("\\multicolumn{3}{l|}{\\emph{Restricted Sample}} & \\multicolumn{2}{c|}{} & \\multicolumn{2}{c}{} \\\\ \n")
pull_tex("china_positive_influence_index", "restricted", "Chinese")
pull_tex("china.best.dev.model",           "restricted", "Chinese")
pull_tex("usa.best.dev.model",             "restricted", "Chinese")
pull_tex("china_positive_influence_index", "restricted", "US")
pull_tex("china.best.dev.model",           "restricted", "US")
pull_tex("usa.best.dev.model",             "restricted", "US")
# FIGURE 2 - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
cat("\\multicolumn{3}{l|}{} & \\multicolumn{2}{c|}{} & \\multicolumn{2}{c}{} \\\\ \n") # Blank
cat("\\multicolumn{3}{l|}{\\bf Effects of Chinese and US aid on liberal democratic values} & \\multicolumn{2}{l|}{} & \\multicolumn{2}{l}{} \\\\ \n ")
cat("\\multicolumn{3}{l|}{\\emph{Full Sample}} & \\multicolumn{2}{c|}{} & \\multicolumn{2}{c}{} \\\\ \n")
pull_tex("lib_dem_val_index", "full", "Chinese")
cat("\\multicolumn{3}{l|}{} & \\multicolumn{2}{c|}{} & \\multicolumn{2}{c}{} \\\\ \n") # Blank
cat("\\multicolumn{3}{l|}{\\emph{Restricted Sample}} & \\multicolumn{2}{c|}{} & \\multicolumn{2}{c}{} \\\\ \n")
pull_tex("lib_dem_val_index", "restricted", "Chinese")
pull_tex("lib_dem_val_index", "restricted", "US")
# FIGURE 3 - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
cat("\\multicolumn{3}{l|}{} & \\multicolumn{2}{c|}{} & \\multicolumn{2}{c}{} \\\\ \n") # Blank
cat("\\multicolumn{5}{l|}{\\bf Effects of Chinese and US aid on perceptions of former colonial powers} & \\multicolumn{2}{l}{} \\\\ \n ")
cat("\\multicolumn{3}{l|}{\\emph{Full Sample}} & \\multicolumn{2}{c|}{} & \\multicolumn{2}{c}{} \\\\ \n")
pull_tex("formcolnpower.best.dev.model", "full", "Chinese")
cat("\\multicolumn{3}{l|}{} & \\multicolumn{2}{c|}{} & \\multicolumn{2}{c}{} \\\\ \n") # Blank
cat("\\multicolumn{3}{l|}{\\emph{Restricted Sample}} & \\multicolumn{2}{c|}{} & \\multicolumn{2}{c}{} \\\\ \n")
pull_tex("formcolnpower.best.dev.model", "restricted", "Chinese")
pull_tex("formcolnpower.best.dev.model", "restricted", "US")
# FIGURE 4 - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
cat("\\multicolumn{3}{l|}{} & \\multicolumn{2}{c|}{} & \\multicolumn{2}{c}{} \\\\ \n") # Blank
cat("\\multicolumn{5}{l|}{\\bf Effects of Chinese aid on factors contributing to positive image of China} & \\multicolumn{2}{l}{} \\\\ \n ")
cat("\\multicolumn{3}{l|}{\\emph{Full Sample}} & \\multicolumn{2}{c|}{} & \\multicolumn{2}{c}{} \\\\ \n")
pull_tex("posimage_productcost", "full", "Chinese")
pull_tex("posimage_supportinintlaffiars", "full", "Chinese")
pull_tex("posimage_noninterference", "full", "Chinese")
pull_tex("posimage_chinesepeople", "full", "Chinese")
pull_tex("posimage_infordevinvetment", "full", "Chinese")
pull_tex("posimage_businessinvetment", "full", "Chinese")
# FIGURE 5 - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
cat("\\multicolumn{3}{l|}{} & \\multicolumn{2}{c|}{} & \\multicolumn{2}{c}{} \\\\ \n") # Blank
cat("\\multicolumn{5}{l|}{\\bf Effects of Chinese aid on factors contributing to negative image of China} & \\multicolumn{2}{l}{} \\\\ \n ")
cat("\\multicolumn{3}{l|}{\\emph{Full Sample}} & \\multicolumn{2}{c|}{} & \\multicolumn{2}{c}{} \\\\ \n")
pull_tex("negimage_productquality", "full", "Chinese")
pull_tex("negimage_landgrabbing", "full", "Chinese")
pull_tex("negimage_takingjobsbusiness", "full", "Chinese")
pull_tex("negimage_resourceextraction", "full", "Chinese")
pull_tex("negimage_cooperateundemocratic", "full", "Chinese")
pull_tex("negimage_chinesecitizenbehavior", "full", "Chinese", 4)
cat("\\hline \n ")
cat("\\end{tabular} ")
sink()
